Enabling accurate and automated identification of wireless devices is critical for allowing network access monitoring and ensuring data authentication for large-scale IoT networks. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitters' inevitable hardware impairments that occur during manufacturing. Although deep learning is proven efficient in classifying devices based on hardware impairments, the performance of deep learning models suffers greatly from variations of the wireless channel conditions, across time and space. To the best of our knowledge, we are the first to propose leveraging MIMO capabilities to mitigate the channel effect and provide a channelresilient device classification framework. We begin by showing that for AWGN channels, combining multiple received signals improves the testing accuracy by up to 30%. We then show that for more realistic Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to 50% when the models are trained and tested over the same channel, and by up to 69% when the models are tested on a channel that is different from that used for training.INDEX TERMS Automated network access, deep learning, IoT device fingerprinting, multiple-input multiple-out (MIMO).
The COVID-19 pandemic has necessitated the development of new virus-control measures, and machine learning (ML) has promise in this area. Our research will investigate into the most current machine learning algorithms used for COVID-19 prediction, with a special emphasis on their potential to optimize decisionmaking and resource distribution during peak pandemic periods. Our review is distinct in that it focuses entirely on machine learning techniques for disease prediction. According to our systematic evaluation of the literature, ML-powered solutions can reduce the strain on healthcare systems. These systems can assess large volumes of health information in order to improve prediction and preventative treatment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.